Reconfigurable Stream-based Tensor Unit with Variable-Precision Posit Arithmetic
Nuno Neves, Pedro Tomás, Nuno Roma · 2020
The increased adoption of DNN applications drove the emergence of dedicated tensor computing units to accelerate multi-dimensional matrix multiplication operations. Although they deploy highly efficient computing architectures, they often lack support for more general-purpose application domains. Such a limitation occurs both due to their consolidated computation scheme (restricted to matrix multiplication) and due to their frequent adoption of low-precision/custom floating-point formats (unsuited for general application domains). In contrast, this paper proposes a new Reconfigurable Tensor Unit (RTU) which deploys an array of variable-precision Vector MultiplyAccumulate (VMA) units. Furthermore, each VMA unit leverages the new Posit floating-point format and supports the full range of standardized posit precisions in a single SIMD unit, with variable vector-element width. Moreover, the proposed RTU explores the Posit format features for fused operations, together with spatial and time-multiplexing reconfiguration mechanisms to fuse and combine multiple VMAs to map high-level and complex operations. The RTU is also supported by an automatic data streaming infrastructure and a pipelined data movement scheme, allowing it to accelerate the computation of most data-parallel patterns commonly present in vectorizable applications. The proposed RTU showed to outperform state-of-the-art tensor and SIMD units, present in off-the-shelf platforms, in turn resulting in significant energy-efficiency improvements.